Multi-Level Factors Associated with Relationship-Centred and Task-Focused Mealtime Practices in Long-Term Care: A Secondary Data Analysis of the Making the Most of Mealtimes Study
Bibliographic record
Abstract
Abstract Mealtimes in long-term care (LTC) can reinforce relationships between staff and residents through relationship-centred care (RCC) practices; however, meals are often task-focused (TF). This cross-sectional study explores multi-level contextual factors that contribute to RCC and TF mealtime practices. Secondary data from residents in 32 Canadian LTC homes were analyzed (n = 634; mean age 86.7 ± 7.8; 31.1% male). Data included resident health record review, standardized mealtime observation tools, and valid questionnaires. A higher average number of RCC (9.6 ± 1.4) than TF (5.6 ± 2.1) practices per meal were observed. Multi-level regression revealed that a significant proportion of variation in the RCC and TF scores was explained at the resident- (intraclass correlation coefficient [ICC]RCC = 0.736; ICCTF = 0.482), dining room- (ICCRCC = 0.210; ICCTF = 0.162), and home- (ICCRCC = 0.054; ICCTF = 0.356) levels. For-profit status and home size modified the associations between functional dependency and practices. Addressing multi-level factors can reinforce RCC practices and reduce TF practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".